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The attributes of specificity and memory enable CD8+ T cells to provide long-lasting protection against a variety of challenges. Although, the importance of CD8+ T cells for protection against intracellular infections and transformation is well-established, the functional type; effector phenotypes (Tc1, Tc2, Tc17 and/or Tcreg) and/or memory (effector or central), of CD8+ T cells most desirable for tumor immunity is not established. To determine the tumor efficacy of various effector types and/or memory CD8 T cells, it is imperative to better understand intrinsic and extrinsic factors that regulate CD8+ T cell differentiation and use this information to generate and test distinct functional cell types in tumor models. The focus of our laboratory investigations is to identify the extrinsic factors such as antigen strength, co-stimulatory molecules, cytokines, and small molecule modifiers that regulate intrinsic programs for various effector and/or memory cell fate in antigen specific CD8 T cells. The use of this information to generate immunity in murine tumor models has facilitated development of new adoptive cell transfer (ACT) as well as immunization strategies for cancer treatment.

This paper proposes a novel tool detection and tracking approach using uncalibrated monocular surgical videos for computer-aided surgical interventions. We hypothesize surgical tool end-effector to be the most distinguishable part of a tool and employ state-of-the-art object detection methods to learn the shape and localize the tool in images. For tracking, we propose a Product of Tracking Experts (PoTE) based generalized object tracking framework by probabilistically-merging tracking outputs (probabilistic/non-probabilistic) from timevarying numbers of trackers. In the current implementation of PoTE, we use three tracking experts -point-feature-based, region-based and object detection-based. A novel point featurebased tracker is also proposed in the form of a voting based bounding box geometry estimation technique building upon point-feature correspondences. Our tracker is causal which makes it suitable for real-time applications. This framework has been tested on real surgical videos and is shown to significantly improve upon the baseline results.

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This research was supported by grants from the National Institutes of Health (HD071408 and HL128628), the March of Dimes, and the W. K. Kellogg Foundation. There were no conflicts or competing interests.